Intrinsic Raman spectroscopy

ABSTRACT

The present invention relates to systems and methods for the measurement of analytes such as glucose. Raman and reflectance spectroscopy are used to measure a volume, of material such as a blood sample or tissue within a subject and determine a concentration of a blood analyte based thereon. The present invention further relates to a calibration method, constrained regularization (CR), and demonstrates its use for analyzing spectra including, for example, the measurement glucose concentrations using transcutaneous Raman spectroscopy.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the priority of U.S. Provisional Application No.60/701,839 filed Jul. 22, 2005 and U.S. Provisional Application No.60/735,986 filed Nov. 10, 2005. The entire contents of the aboveapplications are being incorporated herein by reference.

BACKGROUND OF THE INVENTION

The development of non-invasive systems and methods for thetranscutaneous measurement of blood analytes, such as glucose, remainimportant for the diagnosis and treatment of a variety of conditions.Conventional blood sampling methods are painful and have otherundesirable features. However, none of the non-invasive methods beingdeveloped has demonstrated sufficient accuracy or reproducibility forclinical use. A major obstacle in achieving reproducible opticalmeasurement such as Raman spectroscopy is the variation in opticalproperties (absorption and scattering) from measurement site to site,from subject to subject, and over time. Optical properties are importantbecause the amount of absorption and scattering in a sample greatlyinfluences the volume of tissue sampled by the excitation light and themagnitude of the collected Raman signal. A method to correct for this isnecessary for the success because of the way calibration is performed.Reference concentrations obtained from a blood glucose or interstitialglucose measurement are used to correlate a given Raman spectrum withthe concentration of glucose that spectrum should contain. Significanterrors in calibration transfer are prone to occur if the number ofglucose molecules sampled by the Raman instrument on day two isdifferent than day one and yet the concentration of glucose molecules inthe blood is the same. In other words, spectroscopic techniques such asRaman are sensitive to the number of glucose molecules sampled in theblood-tissue matrix, whereas the reference measurement provides theconcentration (number÷volume) of glucose molecules in the blood orinterstitial fluid.

To further improve optical measurements of blood analytes multivariatecalibration has been used as an analytical technique for extractinganalyte concentrations in complex chemical systems that exhibit linearresponse. Multivariate techniques are particularly well suited toanalysis of spectral data, since information about all the analytes canbe collected simultaneously at many wavelengths. Explicit calibrationmethods are often used when all of the constituent spectra can beindividually measured or pre-calculated. Examples are ordinary leastsquares (OLS) and classical least squares (CLS). When individual spectraare not all known, implicit modeling techniques are often adapted.Principle component regression (PCR) and partial least squares (PLS) aretwo frequently used methods in this category. In either case, the goalof multivariate calibration is to obtain a spectrum of regressioncoefficients, b, such that an analyte's concentration, c, can beaccurately predicted by taking the scalar product of b with a spectrum,s:c═S ^(T) ·b  (1)(Lowercase boldface type denotes a column vector, uppercase boldfacetype a matrix; and the superscript ^(T) denotes transpose.) Theregression vector, b, is unique in an ideal noise-free linear systemwithout constituent correlations, and the goal of both implicit andexplicit schemes is to find an accurate approximation to b for thesystem of interest.

Explicit and implicit methods have their own advantages and limitations.Explicit methods provide transparent models with easily interpretableresults. However, they require high quality spectra and accurateconcentration measurements of each of the constituent analytes (orequivalent information), which may be difficult to obtain, particularlyin in vivo applications. Implicit methods require only high qualitycalibration spectra and accurate concentration measurements of theanalyte of interest, (the “calibration data”), greatly facilitatingsystem design. However, unlike explicit methods, the performance ofimplicit methods can both be simply judged by conventional statisticalmeasures such as goodness of fit. Spurious effects such as system driftand co-variations among constituents can be incorrectly interpreted aslegitimate correlations. Furthermore, implicit methods such as PCR andPLS lack the ability to incorporate information about the system oranalytes, in addition to the calibration data, into b. Such priorinformation can, in principle, improve measurement accuracy. Inparticular, in many cases it is desirable to use prior spectralinformation about the constituent analytes. Such information generallyhelps stabilize and enhance deconvolution, classification and/orinversion algorithms. In multivariate calibration, methods combiningexplicit and implicit modeling, such as hybrid linear analysis (HLA),achieve the same goal by removing the contribution of the known analytespectrum of interest from the sample spectra. Thus, there remains a needfor further improvements in systems and methods for the non-invasivemeasurement of blood analytes.

SUMMARY OF THE INVENTION

The present invention relates to measurements of blood analytes such asglucose concentration in human or animal subjects. Systems and methodscan include non-invasive transcutaneous measurements of glucoseconcentration using Raman and reflectance spectroscopy. Alternatively, aprobe can be for the internal measurement of blood analytes, or inanother embodiment systems and methods can be used for the measurementof blood or tissue samples removed from the body of a subject.

A preferred embodiment includes a system for performing measurements inaccordance with the invention that has a light source for Ramanexcitation and reflectance measurements. In a further embodiment, afirst light source is used for the Raman measurements and a second lightsource is used for reflectance measurements. Light delivery andcollection systems can deliver light onto the surface of the skin of thesubject and collect light returning therefrom. A detector records thecollected Raman and reflectance data and delivers the data to aprocessor for determination of the sampling volume within the tissue,provides a corrected Raman spectrum and a concentration level for theanalyte of interest. A preferred embodiment for the first light sourcecan be a laser emitting in the infrared range of 750 nm to 1200 nm andcan comprise a diode laser emitting at 830 nm, for example. The secondlight source can be a white light source such as a tungsten halogenlamp.

Preferred methods for processing the acquired data can include a partialleast squares (PLS) analysis. PLS with leave-one-out cross validationcan be carried out on data from each individual so that errors fromsite-to-site and individual differences are reduced. The mostsignificant cause of calibration error can be the substantial temporalchanges in the measured Raman spectra that are observed. These temporalchanges are not believed to be a result of instrumental drift orsystematic error and as such are not isolated to a particularinstrument. The large magnitudes of such changes also preclude them frombeing manifestations of blood glucose concentration variation.

In order to equate the measurement of blood glucose molecules, forexample, it is preferable that two conditions be met: (1) that thesampling volume be constant or corrected for and (2) the percentage ofglucose-containing fluid within this volume be constant. The lattercondition may be met by ensuring the same area is sampled, the pressureon the surrounding tissue is similar, and the subject maintains asimilar hydration level from day to day.

In near-infrared absorption experiments, the common solution is to scalethe spectra to the prominent water absorption peak, the assumption beingthat glucose molecules are water-soluble and therefore water serves asan internal standard. However, the effects of sample dehydration in theupper tissue layers during the course of the measurement may limit theusefulness of water as an internal standard. The region of interest(3001800 cm⁻¹) for biological Raman spectroscopy does not have a strongcontribution from water. The strongest water band, at 1640 cm⁻¹, isoverwhelmed by the protein amide band occurring at the same position.Further, adjusting the spectra to the height of a particular peak doesnot account for spectral distortions that may occur as a result oftemperature variations or the presence of an absorption feature.

A preferred embodiment of the invention addresses these temporalvariations using processing that can include determination of a ratio ofthe Raman data with the diffuse reflectance (DR) data to providecorrected spectral data and thereby remove distortions caused by theturbidity of the tissue being measured. This ratio produces an intrinsicRaman spectrum (IRS) that can help reduce temporal changes in themeasurement. Additionally, by determining the sampling volume of thetissue being measured, by dividing the number of glucose moleculesmeasured using Raman excitation by the sampling volume, a quantitativemeasurement of blood glucose concentration is obtained.

The present invention uses reference information with calibration datain an implicit representation. Starting with the inverse mixture modelas the forward problem, the inverse problem has a solution b.Instabilities associated with the inversion process are removed using atechnique known as regularization, and prior information is includedusing a spectral constraint. This method is referred to herein asconstrained regularization (CR).

In a preferred embodiment, human data with elevated glucose levels,obtained via transcutaneous Raman spectroscopy can be analyzed usingthis method. With CR, both the standard error obtained usingleave-one-out cross validation (SEV) and the standard error ofprediction (SPE) improve compared to results obtained using PLS. CRanalysis of blood analytes are also applicable to multivariatetechniques that employ near infrared spectroscopy, as well as Ramanspectroscopy.

An additional factor that greatly affects the performance of thecalibration algorithm is the accuracy of the reference measurements.Previous studies have used the blood plasma glucose concentrationscollected at five minute intervals as the reference concentrations inbuilding the calibration algorithm. However, if the glucose Ramanspectra primarily originates from interstitial fluid, then thedifferences between interstitial fluid and plasma glucose concentrationscan contribute to the error in the measurements.

To address the issues of sampling volume correction and the relativeroles of interstitial fluid and plasma glucose, preferred embodiments ofthe present invention seek to address difficulties with calibrationtransfer which is impeded by variations in sampling volume as a resultof differences in tissue scattering and absorption. By correcting forthe sampling volume with diffuse reflectance spectroscopy, the system isable to apply a calibration obtained at one time in one individual tothat same individual at another time. Additionally, it is noted that theglucose in the interstitial fluid provides a substantial contribution tothe glucose Raman signal, and the lag time between plasma andinterstitial glucose concentrations gives rise to measurement error.Therefore, the information obtained from interstitial fluid glucosemeasurements in combination with those from plasma glucose can be usedto provide a calibration with reduced error, compared to a calibrationderived from plasma glucose measurements alone.

Consequently the combined use of intrinsic Raman spectroscopy to correctfor sampling volume variations and interstitial fluid glucosemeasurements improves noninvasive analysis and can provide successfulcalibration transfer.

DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a Raman signal from human forearm obtained with a KaiserPhAT probe system at 785 nm over the course of 110 minutes (each traceis collected in 1.5 minutes).

FIG. 1B shows mean-centered intensity of several different pixels acrossthe spectral region as a function of time. The fit in red is a doubleexponential function.

FIG. 1C shows mean-centered peak heights of the four asterisked Ramanpeaks in (FIG. 1A) as a function of time, the fit in red is a 2^(nd)order polynomial to guide the eye, but no particular function is knownto fit this decay.

FIG. 2 shows simulation results of prediction error for varioussuperimposed temporal decays: none, single exponential decay 80%, 70%and double exponential decay 70%/60%. For each temporal decay, twodifferent signal-to-noise ratios were considered.

FIG. 3 illustrates a preferred embodiment of a system for obtainingRaman and reflectance data in accordance with the invention.

FIG. 4A shows another preferred embodiment of a light delivery system inaccordance with the invention.

FIG. 4B illustrates a side looking fiber optic probe.

FIG. 4C shows a distal end of a fiber optic probe for use within a humanor animal body.

FIG. 4D illustrates a forward looking fiber optic probe or catheter witha ball lens.

FIG. 4E illustrates a forward looking probe or catheter with a half balllens.

FIG. 5 shows a tissue phantom for use in conjunction with a preferredembodiment of the invention.

FIG. 6 shows a tissue phantom concentration profile.

FIGS. 7A and 7B show Raman spectra and components of the tissue phantom,respectively.

FIGS. 8A and 8B show diffuse reflectance and correct Raman spectra,respectively.

FIGS. 9A and 9B show plots of spectra for glucose and creatinine,respectively, using ordinary least squares.

FIGS. 10A and 10B show plots with corrected Raman data for glucose andcreatinine, respectively.

FIGS. 11A and 11B show Raman and corrected Raman data, respectively, forglucose using a partial least squares analysis.

FIGS. 12A and 12B illustrate OLS spectral components and a measuredspectrum with the OLS fit and residual, respectively.

FIGS. 13A and 13B graphically illustrate the Raleigh peak area (opencircles) and calculated diffuse reflectance, R_(d) (solid line) plottedversus the ration μ_(s) ¹/μ_(a), and the measured Raman signal, which islinear with respect to the Rayleigh peak area squared, respectively.

FIGS. 14A and 14B graphically illustrate the measured Raman signal as afunction of μ_(a) and μ_(s), respectively, before (squares) and after(circles) correction. The symbol marks the median value of the Ramansignal for the opposing optical property and the bar extends to themaximum and minimum of the optical range.

FIG. 15 includes Raman spectra of the ten components used in thesimulation: (C): creatinine; (U): urea; (G): glucose; (S_(c)):representative clear sample spectrum and (S_(t)): representative turbidsample spectrum.

FIG. 16 shows SEP vauves for glucose.

FIG. 17A shows (a) b_(OLS) (b) b_(PLS)-b_(OLS) and (c) b_(CR)-b_(OLS).

FIG. 17B shows SEP valves for glucose for clear and turbid samples.

FIG. 18A shows a plot for corrected Raman data.

FIG. 18B illustrates a process for using constrained regularization tocalculate a spectrum.

DETAILED DESCRIPTION OF THE INVENTION

Preferred embodiments of the present invention relate to the use ofRaman spectroscopy for the measurement of blood analytes. Systems andmethods used in conjunction with preferred embodiments include thosedescribed in U.S. Pat. No. 5,615,673 and in U.S. ApplicationsPCT/US96/04136, 60/675,252 filed on Apr. 27, 2005, 60/701,839 filed onJul. 22, 2005, 60/702,248 filed on Jul. 25, 2005, and Ser. No.11/412,418 filed Apr. 27, 2006 this patent and these applications beingincorporated by reference into the present application in theirentirety.

FIG. 1 depicts the signal obtained from a human forearm as observed witha commercial Kaiser PHAT probe Raman system with 785 nm excitation. Thebackground signal, after spectral correction for the combined responseof the grating and the CCD. Each trace is obtained with a 1.5 minutecollection time and the overall signal level is seen to decrease overthe course of the nearly 2 hour measurement. The intensity decays acrossthe spectral region are shown in FIG. 1B. The fit, shown in red, is adouble-exponential function. The background decay in Raman experimentshas been observed to be well fit with a double exponential function. Thepeak heights of the four asterisked Raman peaks in FIG. 1A are plottedas a function of time in FIG. 1C. The peak heights are determined by alinear interpolation of the baseline under the peak. The fit, shown inred, is a 2^(nd) order polynomial although an exponential function couldalso have been used. The peak height data are noisy owing to thedifficulty in extracting the background without modifying the peakheight and because of likely sampling effects such as movement and bloodflow. However, it is apparent that the Raman signal is changing overtime. The overall decrease in Raman signal has been observed to be onthe order of 7-15% over the course of 2-3 hours for different humansubjects.

The downward trend in Raman peak heights as a function of time can be aresult of laser-tissue interactions such as local heating-inducedchanges in the scattering properties of the sample, sample dehydration,heating-induced changes in the water absorption coefficient, etc. Thesevariations, although unrelated to glucose levels, can be recognized as alegitimate correlation by the multivariate calibration algorithm andconsequently impair the calibration results. To demonstrate howdetrimental such spurious correlations can be to accurate prediction, a10-component representation can be used to perform numericalsimulations. For each calibration set, 25 spectral samples are formedwith the pattern of glucose concentration variation designed to mimic anoral glucose tolerance test and other 9 interfering components (actin,cholesterol, collagen I, collagen III, water, hemoglobin, keratin,phosphatidylcholine and triolein) according to their magnitude in our invivo background-removed skin Raman spectra. Within the 2 to 3 hour timeperiod these 25 spectra are designed to simulate, the glucoseconcentration starts at ˜90 mg/dL, rises linearly to ˜220 mg/dL, andfalls back to ˜90 mg/dL. To examine the effects of a decreasing overallRaman signal level, a temporal exponential decay is superimposed ontothe time-sequenced Raman spectra. The pattern of the exponential decayis controlled by one parameter: the peak intensity ratio between thelast and the first spectral sample.

The representation considers all components experiencing the sameoverall signal decay, imitating a possible inner-filter effect. Thisexamines single-exponential decay profiles towards 80% and 70% as wellas a double-exponential decay profile towards 70%/60% for two differentsignal-to-noise ratios. FIG. 2 displays the results, which suggest thatthe error inflation is roughly linear with the amount of signal decay.For example, an exponential decay that decreases towards 80% of itsinitial peak height gives rise to 20% more error in prediction. It isemphasized again that such a decrease does not exactly follow thepattern of the glucose concentration variation; however its existenceimpairs the prediction accuracy due to the possibility of chancecorrelation to glucose.

This illustrates that even if the background signal, which itself isdecreasing, can be completely removed from the spectrum withoutinfluencing the Raman peaks, a condition that is not guaranteed and willbe discussed below. The non-glucose related changes in Raman peakintensity must be addressed. Because the laser power is not fluctuatingby a significant amount, a provision that is ensured in the system bymeasuring the power with a beam splitter and photodetector duringmeasurements, the only likely mechanism that can lead to a change inRaman signal level is optical property (absorption and scattering)changes of the sample.

Thus the present invention relates to the use of quantitative orintrinsic Raman spectroscopy, a technique to remove turbidity-inducedspectral distortions to provide transcutaneous measurements of bloodglucose and other blood analytes. The turbidity-free Raman spectra canoriginate from different sampling volumes, depending on the site and onthe individual, and therefore may incorporate different amounts ofglucose molecules. This is possible because human skin is a layeredstructure with glucose distributed in a highly non-uniform fashion. Therelative thickness of each layered structure varies from site to site onan individual and from individual to individual.

A method to determine the sampling volume during a given Ramanmeasurement would thus be useful. Reference concentrations obtained froma blood measurement (finger stick) can be used to correlate a givenRaman spectrum with the concentration of glucose that spectrum contains.Significant errors in calibration are prone to occur if differentindividuals, or the same individual on a different day, who have thesame level of glucose in their bloodstream as determined by a clinicalinstrument, have sufficiently different skin morphology such that theactual number of glucose molecules sampled by the Raman instrument isnot the same. In other words, spectroscopic techniques such as Ramanmeasurements are sensitive to the number of glucose molecules sampled inthe blood-tissue matrix whereas the reference measurement provides theconcentration (number divided by volume) of glucose molecules in theblood. In order to equate these measurements, it is preferable that twoconditions be met: (1) the concentration of glucose in the combinedinterstitial fluid and blood capillaries be within a constant ofproportionality to that in venous blood. This has been shown to be true,albeit the glucose concentration in interstitial fluid lags behind thatin the blood by about 4-10 minutes, (2) the volume of theseglucose-containing regions that are sampled in a given measurement canbe determined.

In noninvasive measurements using near-infrared absorption spectroscopy,the latter condition is met by scaling spectra to the prominent waterpeak; the assumption being that glucose molecules are water-soluble andtherefore water serves as an internal standard. It is unclear whateffect sample dehydration in the upper tissue layers over the course ofthe measurement has on this method. Regardless, the Raman spectrum oftissue in the region of interest for many molecules (300-1800 cm⁻¹) doesnot have this option because the water band at 1640 cm⁻¹ is overlappedby the amide band.

Additionally, owing to the spatially heterogeneous distribution ofglucose in the blood-tissue matrix, collecting Raman light from theentire 3D sampling volume of the excitation laser inevitably includes alarge amount of undesired constituent Raman signatures, such as those ofkeratin and collagen. These Raman signatures not only generate extracounts of shot noise, but also interfere with the subsequent dataanalysis and compromise the overall performance.

The optical properties of a biological material or tissue are known tobe affected by laser irradiation, typically as a result of laserheating. Much of the emphasis has been placed on large-scale temperaturechanges that result in tissue denaturation or coagulation. These changesare typically irreversible and occur at temperatures above 50 degreesCelsius. Smaller, reversible changes of optical properties occur atlower temperatures and signify effects ranging from thermal lensing(gradient in the index of refraction caused by localized heating) tothermal expansion of tissue. Additionally, the absorption coefficient ofwater has been shown to be highly dependent on temperature. Of thevariety of thermo-optically induced scattering phenomena, two are knownto have the effect of decreasing the diffuse reflectance (DR) of thetissue sample: changes in shape or size of scatterers resulting in adecrease of the reduced scattering coefficient (μ_(s)′) and localdehydration that may increase the anisotropy of the cells towardsforward scattering. The present invention utilizes the effects that leadto a reduction in diffuse reflectance because of the observed decreasingtrend in Raman signal level. Diffusely reflected light is that which hasundergone numerous elastic scattering events before escaping the tissueand thereby provides a metric for the amount of tissue absorption andscattering. The optical properties of a given sample can therefore bemeasured in situ by diffuse reflectance spectroscopy (DRS).

The present invention thus corrects not only for spectral distortionsdue to dispersions of endogenous absorbers and scatterers, but also forthe changes in these properties over time and across samples. Hence, acontinuous measurement of the DR can be used to offset any bulk opticalproperty changes over the course of a Raman spectroscopy measurement.

Apart from the dynamic variations due to optical property changes uponlaser-tissue interaction, existing differences across measuring sites orpersons, for instance, as a result of slight variations in skin layerthickness, can limit the performance of a multiple-day or amultiple-sample measurement. To this end, a method to determine thevolume of glucose-containing regions that are sampled in eachmeasurement can be generated employing analytical models.

In the photon migration picture for light propagation in turbid media,diffuse reflectance can be characterized by three parameters: absorptioncoefficient (μ_(a)), scattering coefficient (μ_(s)), and scatteringanisotropy (g). Extraction of these parameters from the DR spectra hasbeen demonstrated. The present invention uses a light delivery andcollection system in the wavelength range of interest, i.e., 830-1000nm.

Further, with an efficient Monte Carlo method the effects of the layeredstructure of skin on its Raman spectrum is analyzed. Because most Ramanscatterers have a specific spatial distribution in skin, such as keratinin the epidermis, collagen in the dermis, etc., a single homogeneouslayer model does not fully represent the sample. A method has beendeveloped for a two-layer structure in the spectroscopic diagnosis ofprecancerous cervical tissue using fluorescence spectroscopy.

As a starting point, note that human forearm skin can be represented asa two-layer structure composed of epidermis (including stratum corneum)and dermis, with homogeneity assumed within each individual layer. Sucha distinction is made because epidermis is abundant in keratin and lacksglucose whereas dermis is rich in collagen (type 1) and containsinterstitial as well as capillary glucose. Because keratin and collagenhave such distinctive spatial distributions, information can be obtainedabout the thicknesses of the epidermis and dermis by comparing therelative magnitude of keratin and collagen Raman signals. This isdependent upon knowledge of the Raman scattering cross sections forkeratin and collagen. Analytical models, using DRS and IRS, allowinversion and estimation of optical properties of each individual layer.Such estimates enable determinations of the exact sampling volume and touse it for calibrating the reference glucose concentration. In otherwords, by knowing the exact sampling volume and its coverage of variousskin morphological structures, the method can estimate how much of theglucose-containing region (dermis in the two-layer model) is sampled.Note that this scaling value can differ from site to site and fromperson to person. Multiplication between the estimated glucose-specificsampling volume and the reference glucose concentration results in thenumber of glucose molecules actually sampled, which can be used incalibrating the Raman spectra. Using this calibration algorithm, thenumber of glucose molecules measured by Raman measurement can be dividedby the sampling volume to obtain the blood glucose concentration.

Measurements can also be conducted on tissue phantoms with layeredstructures of known concentrations of Raman scatterers to demonstratethe efficacy of this technique. The combined use of Raman spectroscopyand DRS offer a method to correct for skin composition diversity.

For measurements involving the simultaneous measurement of DR and Ramanspectra, a bimodal instrument is required. The light delivery system 10can employ a laser 12 emitting a narrowband pulse and a white lightsource 20 emitting a broadband signal and the collection system 40 caninclude the collection optics 42, 44, 46, 48, spectrometer 54, and CCDdetector 58. FIG. 3 is an illustration of a preferred embodiment of theinvention. Fiber bundle 48 collects over an area 50 and converts thisarea into a linear array 52 for coupling to the spectrograph dispersingelement 56. A controller can be used to actuate the light sources. Thecontroller an be used to actuate a shutter device.

As the embodiment uses two different excitation sources while retaininga single spectrometer, laser excitation of the sample and white lightexcitation of the sample must alternate. Shutters 14 and 22 can controlcoupling of light to beamsplitter 24, steering mirror 28 and lens 30.The duration and the frequency of the application of each excitationsource can be used to correct for optical property changes while stillobtaining high signal-to-noise Raman spectra. The light from bothsources is directed on the same path to ensure that the excitation spoton the tissue is equivalent for both the laser and the white lightsource. Furthermore, the tungsten halogen lamp can be appropriatelyfiltered such that light within the wavelength range of 830-1000 nm istransmitted. The out-of-range light is excluded to reduce stray lightinside the spectrometer and possible heating of the tissue.

In the Raman mode of operation, a bandpass filter 16 that passes only830 nm (6 nm FWHM) is necessary. A photodiode 26 monitors light source12, 20 output for stability.

This system allows for the collection of DR from the same sample andunder the same conditions that Raman spectra are acquired. Theacquisition of DR enables the correction of the Raman spectra fortime-dependent changes in optical properties of the sample and fordifferences in sampling volume.

Another preferred embodiment of the light delivery system is illustratedin FIG. 4A. In this embodiment, optical fibers 62, 64 couple the twolight sources through a fiber combiner 66 into a single optical fiber67. A fiber collimator 68 than directs the light through a switchedlaser line filter 70, a telescopic lens 74 and a paraboloidal mirror 42onto the patient or sample 75. This filter 90 must be removed in the DRSmode of operation to allow light in the described wavelength range topass unobstructed. Thus, the laser line filter can be mounted on asoftware-controlled solenoid 92 synchronized with mode switching.

Another preferred embodiment can employ a fiber optic probe or catheterfor use within body cavities, lumens, arteries, the heart,gastrointestinal tract, etc. to measure body tissue or fluids todetermine the presence and/or concentration of analytes and to diagnosedisease or other abnormalities.

The distal end of such a probe 100 can have a central excitation opticalfiber 110 that is surrounded by aluminum jacket 112 as shown in FIG. 4B.The proximal end of the excitation fiber can be coupled to either one ortwo light sources as described herein. This particular embodiment is 2mm in diameter, however, the device can have diameters of 1-3 mm for usein the arterial system or larger diameters for other body cavities.Outer wall or jacket 108 surrounds the flexible portion of the catheter100.

The fiber 110 is filtered by a short pass filter rod 116 at the distalend which is surrounded by metal sleeve 118. The filter 116 couples theexcitation pulse to a half ball lens 102, reflected off of mirror 104and through sapphire window 106. The light returning from the tissue iscollected along a path through window 106, reflected at 104, transmittedthrough lens 102 and collected by collection fibers 114. An end crosssectional view of the fibers is shown in FIG. 4C. The ball lens 160covers the distal end with the central excitation fiber 110 (200 μmfiber with NA=0.22) surrounded by three pairs of collection fibers 140used to collect Raman spectra. Each pair of the Raman fibers isseparated by a collection fiber 150 (200 μm fiber with NA=0.26) that isused to collect a reflectance spectrum. The filter 120 (such as a longpass filter tube) is selected to transmit the desired collectionspectrum which can be different depending on the Raman or reflectancespectrum being collected by a particular fiber.

In FIG. 4D a forward looking ball lens 160 is used. A forward lookingcatheter system employing a half ball lens system 170 as shown in FIG.4E can provide a larger contact surface 172 at the distal end of thedevice.

FIG. 5 is a tissue phantom having the indicated distributions ofglucose, creatinine and intralipid. Such a phantom can be used forcalibration measurements. FIG. 6 shows the phantom concentration profilefor the indicated components.

The Raman spectra of the phantom is shown in FIG. 7A with the individualcomponents shown in FIG. 7B. The diffuse reflectance spectra (DRS) areshown in FIG. 8A and the intrinsic Raman spectra obtained from themeasured Raman (FIG. 7A) and DRS are shown in FIG. 8B.

In FIG. 9A the actual glucose concentration is plotted for comparisonwith ordinary least squares analysis of the measured Raman spectrum. Thecomparative plot for creatinine is shown in FIG. 9B.

FIGS. 10A and 10B illustrate the improvement in measured data using anordinary least squares analysis for the intrinsic Raman spectra. Usingthe intrinsic Raman spectra improvements of 73% and 74% were observed inthe glucose and creatinine measurements, respectively.

FIGS. 11A and 11B show the collected Raman spectra for glucose and thecorrected IRS spectra, respectively, using a partial least squaresmultivariate calibration. The regression vector b_(PLS) is described ingreater detail hereinafter and in U.S. Application No. 60/701,839 filedon Jul. 22, 2005, the entire contents of which is incorporated herein byreference. The IRS spectrum has a clear correlation with the glucosepeaks at the bottom of both spectra.

For applications in which μ_(a) and μ_(s) are relatively constant overthe collected wavelength range, the following embodiment of theinvention relates to a simple and effective correction for changes insampling volume that does not require an additional light source ordetector. This method utilizes the intensity of the light collected atthe excitation wavelength, also referred to as the Rayleigh peak, toprobe sample optical properties. Because the Rayleigh peak providesinformation at only the excitation wavelength, wavelength-dependentvariations owing to prominent absorption bands are not corrected.However, for many applications, such as Raman measurements of biologicalmedia in the near-infrared (NIR) region, μ_(a) and μ_(s) are only weaklydependent on wavelength, and thus the method presented here offers auseful measurement. In a preferred embodiment, the Raman excitationwavelength is in a range of 750 nm to 950 nm. This avoids a prominentwater absorption peak above 950 nm in blood or tissue, for example.

The following measurements illustrate the effect of turbidity on theRayleigh peak and an analyte Raman signal. The measurements indicatethat the Rayleigh peak is a measure of diffuse reflectance and thatRaman intensity and the Rayleigh peak intensity are correlated.

In the following measurements of 49 tissue phantoms in water solutions,following a 7×7 matrix of scattering and absorption properties withranges similar to that found in biological tissue. The scatteringcoefficient, μ_(s), was varied from 24 to 130 cm⁻¹ (median 81.6 cm⁻¹) at830 nm by altering the concentration of Intralipid (Baxter Healthcare),an anisotropic elastic scatterer commonly used to simulate tissuescattering. The absorption coefficient, μ_(a), was varied from 0.08 to1.3 cm⁻¹ (median 0.31 cm⁻¹) at 830 nm by altering the concentration ofIndia ink (Speedball), which possesses a nearly flat absorption profilein the NIR region. Optical properties of representative tissue phantomswere determined by integrating sphere measurements. A constant 50 mMconcentration of creatinine was included in each sample to serve as anindicator of the Raman signal. The relatively high concentration ofcreatinine enabled higher absorption values to be analyzed whileretaining a satisfactory signal-to-noise ratio.

The measurement employed a system such as that illustrated in FIG. 3with a 830 nm diode laser (Process Instruments) as the excitation source(without using a second broadband source), with power monitored by anexternal photodiode. The laser was focused through a small hole (4 mmdia.) in an off-axis, gold-coated, half-paraboloidal mirror(Perkin-Elmer) and into a fused silica cuvette (1 cm pathlength) filledwith the sample of interest. The laser spot diameter and power at thesample were approximately 1 mm and 250 mW, respectively. Back-scatteredRaman and diffusely reflected excitation light were collected with theparaboloidal mirror and sent through a holographic notch filter (Kaiser)to reduce the magnitude of the Rayleigh peak. Specular reflection fromthe cuvette surface passed through the hole in the paraboloidal mirrorand was significantly diminished. The collected light was then focusedinto a fiber bundle (Romack) that transforms the circular shape of thecollected light into a vertical line (˜400 mm×26 mm). The exit end ofthe fiber bundle serves as the entrance slit of a modified f/1.4spectrometer (Kaiser). The light was dispersed with a holographicgrating onto a 1300×340 pixel liquid nitrogen-cooled CCD detector(Princeton Instruments). Care was taken to ensure that the Rayleigh peakfrom the highest scattering samples did not saturate the detector.Spectra were accumulated with a 2 s integration time, and 10 sequentialspectra were collected for each sample. Identical excitation-collectiongeometry was maintained throughout the measurement by fixing the cuvetteposition. Samples were replaced via pipette following a water rinse andtwo rinses of the sample of interest. Data were processed off-line forimage curvature correction, summation, and removal of cosmic rays.Spectra from 280-1700 cm⁻¹ (850-966 nm) were used in all data analysis.

Data were analyzed via ordinary least squares (OLS) using aseven-component model. The model components included fused silica(cuvette), water, Intralipid, India ink, creatinine (as measured inwater, with the background subtracted), fluorescence (from impurities inthe cuvette—obtained by subtracting the tenth spectrum from the firstspectrum for a representative sample), and a DC offset to account forthe increased or decreased signal level due to scattering or absorption,respectively. The OLS model components are shown in FIG. 12A. Eachspectrum was fit individually to account for varying levels offluorescence and offset and the creatinine fit coefficients for the 10spectra in each set were averaged for each sample. A representativespectrum, OLS fit, and residual are shown in FIG. 12B. The residualcontains no appreciable structure, supporting the assertion thatspectral shape distortions owing to optical property variations over ourcollected wavelength range are minimal.

The Rayleigh peak was integrated for each spectrum, averaged for eachsample, and then normalized to the highest value, which occurred for thesample with highest scattering and lowest absorption. The Rayleigh peakintensity dropped to as low as 20% of the highest value for the samplewith lowest scattering and highest absorption. The laser power, however,fluctuated by no more than 0.25% over the course of the experiment.Contributions to the Rayleigh peak from specular reflections areinsubstantial with the system used for these measurements. Thus, anyvariation in the Rayleigh peak intensity can be attributed to sampleoptical property effects.

With certain collection-excitation geometries diffuse reflectance may becharacterized by a single parameter: the ratio μ_(s)′/μ_(a), whereμ_(s)′ is the transport scattering coefficient, (1−g)μ_(s), with g=0.8for intralipid at the excitation wavelength. A simple exponential modelfor diffuse reflectance has been derived and shown to be representativeof experimentally-obtained diffuse reflectance by Fabbri, et. al. (seeAppl. Opt. 42, 3063 (2003) incorporated herein by reference):R_(d)=exp{−A/[3(1+μ_(s)′/μ_(a))]^(1/2)}. The A parameter in thisexpression depends on the refractive index mismatch and the ratioμ_(s)′/μ_(a). For the experimental work performed by Fabbri, et al., thevalue of A was set to a constant 7.8, which is for a refractive indexmismatch of 1.33. In these measurements, the solutions were contained ina fused silica cuvette with refractive index 1.46. To determine theoptimal value for A to fit this function to our Rayleigh peak area data,an iterative procedure based on least-squares fitting was employed.Because this measures relative and not absolute reflectance values, thenormalization factor for the Rayleigh peak area data was also determinedby the iterative process. The values for A and the normalization factorwere found to be 6 and 0.84, respectively. The normalized data and fitare plotted in FIG. 13A versus μ_(s)/μ_(a). The agreement between thedata and the calculated reflectance is likely due to the largecollection area of our paraboloidal mirror. From this, it can beconcluded that the Rayleigh peak is a suitable measure of diffusereflectance at 830 nm.

The OLS fit coefficient for creatinine, which serves as the indicator ofRaman intensity, is hereafter referred to as the measured Raman signal.In the absence of turbidity, this value should be 1 for all samples, asthe concentration of creatinine was constant. However, owing to opticalproperty changes, measured values ranged from 0.48 to 1.88, a deviationof over 140%. The measured Raman signal is also a function of the ratioμ_(s)′/μ_(a), but with a minor additional dependence on μ_(a). The datareveal a quadratic relationship between the measured Raman signal andthe Rayleigh peak area (FIG. 13B). This relationship can be used tocorrect the variations in Raman intensity. The raw Raman spectra wereeach divided by its Rayleigh peak area scaled according to the quadraticfit of FIG. 13B. The spectra were again fit with the OLS modelcomponents to extract the corrected Raman signal (creatinine fitcoefficients).

The Raman signal for each of the 49 samples is displayed as a functionof μ_(a) (FIG. 14A) and μ_(s) (FIG. 14B), before and after correction.Symbols are used to depict the median value of the Raman signal for theopposing optical property, and the bars extend across the opticalproperty range. Note that the corrected Raman signal is nearly 1 for allsamples, regardless of the optical property variations, indicating thatsampling volume variations have been rectified. As a result, theprediction accuracy is significantly improved, with the root meanstandard error of prediction (RMSEP) for the uncorrected data at 41.5%versus an RMSEP for the corrected data at 7%.

These measurements indicate that information at the excitationwavelength can be effectively used to correct turbidity-inducedintensity distortions in Raman spectra and significantly improveprediction accuracies. The success of this method is, however, dependenton μ_(a) and μ_(s) being relatively constant over the collectedwavelength range. In this case, intensity distortions from samplingvolume variations outweigh spectral shape distortions. In cases wherespectral distortions are comparable to or greater than intensitydistortions, such as in the presence of narrow absorption features, afull spectral range correction using an additional broadband source mayoffer improved results.

Multivariate calibration is an analytical technique for extractinganalyte concentrations in complex chemical systems that exhibit linearresponse. Multivariate techniques are particularly well suited toanalysis of spectral data because information about all of the analytescan be collected simultaneously at many wavelengths.

Explicit and implicit multivariate calibration methods have their ownadvantages and limitations. Explicit calibration methods are often usedwhen all of the constituent spectra can be individually measured orpre-calculated. Examples are ordinary least squares (OLS) and classicalleast squares (CLS). Explicit methods provide transparent models witheasily interpretable results. However, highly controlled experimentalconditions, high quality spectra, and accurate concentrationmeasurements of each of the constituent analytes (or equivalentinformation) may be difficult to obtain, particularly in biomedicalapplications.

When all of the individual constituent spectra are not known, implicitcalibration methods are often adopted. Principal component regression(PCR) and partial least squares (PLS) are two frequently used methods inthis category. Implicit methods require only high quality calibrationspectra and accurate concentration measurements of the analyte ofinterest—the calibration data—greatly facilitating system design.However, unlike explicit methods, the performance of implicit methodscannot be simply judged by conventional statistical measures such asgoodness of fit. Spurious effects such as system drift and co-variationsamong constituents can be incorrectly interpreted as legitimatecorrelations. Furthermore, implicit methods such as PCR and PLS lack theability to incorporate additional information beyond the calibrationdata about the system or analytes. Such prior information has thepotential to improve implicit calibration and limit spuriouscorrelations.

The incorporation of prior information into models has been extensivelypursued in fields such as pattern recognition, machine learning andinverse problems. The use of prior information generally helps stabilizeand enhance deconvolution, classification and/or inversion algorithms.In multivariate calibration, methods combining explicit and implicitschemes have been explored. Owing to prior information about modelconstituent(s), measurement error variance, or the analyte of interest,these methods in principle outperform those without prior information.However, depending on how prior information is incorporated, thesemethods may lack robustness due to inaccuracy in the prior information,especially for methods incorporating known analyte spectra, such ashybrid linear analysis (HLA).

HLA utilizes a separately measured spectrum of the analyte of interesttogether with the calibration data and outperforms methods without priorinformation such as PLS. However, because HLA relies on the subtractionof the analyte spectrum from the calibration data, it is highlysensitive to the accuracy of the spectral shape and its intensity. Forcomplex turbid samples in which absorption and scattering are likely toalter the analyte spectral features in unknown ways, the performance ofHLA is impaired. To provide transcutaneous measurement of blood analytesin vivo, a method has been employed that is more robust againstinaccuracies in the previously measured pure analyte spectra.

A preferred embodiment of the present invention employs a method thatuses prior spectral information with calibration data in an implicitcalibration scheme. Starting with the inverse mixture model as theforward problem, define the inverse problem with solution b.Instabilities associated with the inversion process are removed by meansof a technique known as regularization, and prior information isincluded by means of a spectral constraint. This method is defined asconstrained regularization (CR). The effectiveness of CR using numericalsimulations is demonstrated using measured Raman spectra. With CR, thestandard error of prediction (SEP) is lower than methods without priorinformation, such as PLS, and is less affected by analyte co-variations.Also, CR is more robust than previously developed hybrid method, HLA,when there are inaccuracies in the applied constraint, as often occursin complex or turbid samples such as biological tissues. Note that theterms prior information and spectral constraints are usedinterchangeably for both CR and HLA hereinafter.

Multivariate calibration can be viewed as an inverse problem.Regularization methods, also known as ridge regression in thestatistical literature, are mostly used on ill-conditioned inverseproblems such as tomographic imaging, inverse scattering and imagerestoration. These methods seek to obtain a source distribution in thepresence of noisy (system-corrupted) data. In the present system, thenoise is treated as uncorrelated, which simplifies the analysis.

Implicit calibration schemes require a set of calibration spectra, S,with each spectrum occupying a column of S, associated with severalknown concentrations of the analyte of interest that are expressed as acolumn vector, c, the j^(th) element of which corresponds to the j^(th)column of S. Developing an accurate regression vector, b, requiresaccurate values of c and S. The forward problem for our calibrationmethod is defined by the linear inverse mixture model for a singleanalyte:c=S ^(T) b.  (2)

The goal of the calibration procedure is to use the set of data [S,c] toobtain an accurate b by inverting Eq. (2). The resulting b can then beused in Eq. (1) to predict the analyte concentration, C, of anindependent prospective sample by measuring its spectrum, s. The“accuracy” of b is usually judged by its ability to correctly predictconcentrations prospectively via Eq. (1).

There are two primary difficulties in directly inverting Eq. (2). First,the system is usually underdetermined, i.e., there are more variables(e.g., wavelengths) than equations (e.g., number of calibrationsamples). Thus, direct inversion does not yield a unique solution.Second, even if a pseudo-inverse exists and results in a uniquesolution, such a solution tends to be unstable because all measurementscontain noise and error. That is, small variations in c or S can lead tolarge variations in b. Therefore, a more robust solution is required.

The inversion process can be viewed in terms of singular valuedecomposition (SVD), in which the spectra of the sample set, S, aredecomposed into principal component directions, v_(j), with amplitudesgiven by their respective singular values, σ_(j). Most of theinformation in S is captured in the principle components with largeσ_(j). The singular values with small amplitudes, although potentiallyimportant, are the main cause of instability. Methods to alleviate suchinstabilities are based on reducing the influence of these smallsingular values, accomplished by means of a regularization parameter, Λ.The regularized solution for b is given by:

$\begin{matrix}{{b = {\sum\limits_{j = 1}^{p}{f_{j}\frac{u_{j}^{T}c}{\sigma_{j}}v_{j}}}},{with}} & ( {3a} ) \\{{f_{j}(\Lambda)} = {\frac{\sigma_{j}^{2}}{\sigma_{j}^{2} + \Lambda^{2}}.}} & ( {3b} )\end{matrix}$

Where u_(j) and v_(j) are the eigenvectors of S^(T)S and SS^(T),respectively, and p the rank of S. Note that for σ_(j)>>Λ, f_(j)≅1, andfor σ_(j)<<Λ, f_(j)≅σ_(j) ²/Λ². Thus, one can interpret regularizationas providing a smoothing filter f_(j) that limits the importance of thesmall singular values. For Λ=0, Eq. (3) reduces to the least squaressolution for b. In PCR, Λ=0 and only the k largest singular values (k<p)are used. In Wiener filtering, Λ is chosen to be the noise-to-signalratio.

Equation (3) is the regularized solution of Eq. (2), i.e., no priorinformation is included except by forcing the solution to be finite.However, Eq. (3) can be modified to incorporate prior information. Aconvenient way to accomplish this is by viewing regularization as theminimization of a quadratic cost function Φ:Φ(Λ,b ₀)=∥S ^(T) b−c∥ ² +Λ∥b−b ₀∥²,  (4)with ∥a∥ the Euclidean norm (i.e., magnitude) of a, and b_(o) a spectralconstraint that introduces prior information about b. The first term ofΦ is the model approximation error, and the second term is the norm ofthe difference between the solution and the constraint, which controlsthe smoothness of the solution and its deviation from the constraint. Ifb_(o) is zero, the solution to minimize Φ is given by Eq. (3). Asmentioned above, for Λ=0 the least squares solution is then obtained. Inthe other limit, in which Λ goes to infinity, the solution is simplyb=b_(o). In the following, a calibration method is selected in whichregularization with a properly chosen spectral constraint, b_(o), isemployed, hence the name constrained regularization (CR).

The CR solution, a generalization of Eq. (3), can be analyticallyderived in SVD form as:

$\begin{matrix}{b = {\sum\limits_{j = 1}^{p}{\{ {{{f_{j}(\Lambda)}\frac{u_{j}^{T}c}{\sigma_{j}}} + {( {1 - {f_{j}(\Lambda)}} )v_{j}^{T}b_{0}}} \}{v_{j}.}}}} & (5)\end{matrix}$One choice for b_(o) is the spectrum of the analyte of interest orreference spectrum because that is the solution for b in the absence ofnoise and interferents. Another choice is the net analyte signalcalculated using all of the known pure analyte spectra. Such flexibilityin the selection of b_(o) is owing to the manner in which the constraintis incorporated into the calibration algorithm. For CR, the spectralconstraint is included in a nonlinear fashion through minimization of Φ,and is thus termed a “soft” constraint. On the other hand, there islittle flexibility for methods such as HLA, in which the spectralconstraint is algebraically subtracted from each sample spectrum beforeperforming PCA. This type of constraint can be referred to as a “hard”constraint. CR and HLA are examples showing that the type of constraintaffects the robustness of hybrid methods.

Once b_(o) is chosen, application of CR is straightforward, as Eq. (5)is a direct solution of b and easy to evaluate. A trial value of Λ isselected and b is calculated from Eq. (5) using leave-one-out crossvalidation on the calibration data set to obtain a trial predictionresidual error sum of squares (PRESS):PRESS=Σ(c _(i) −ĉ _(i))²,  (6)where c_(j) and ĉ_(j) are reference and predicted concentrations,respectively, and i denotes the sample index. Λ is then varied until theminimum PRESS value obtained. The resulting value of Λ is then used withthe full calibration data set, [S,c], to calculate b. This regressionvector or transformation can then be used to predict the concentrationsof prospective samples with SEP values calculated by the followingformula:

$\begin{matrix}{{{SEP} = \sqrt{\frac{\sum\limits_{i = 1}^{n}{{c_{i} - {\hat{c}}_{i}}}^{2}}{n}}},} & (7)\end{matrix}$with n the number of samples in the prospective data set. It is usefulto denote the b vector obtained from a particular method herein asb_(method).

Numerical spectra were generated by forming linear combinations ofconstituent analyte spectra of glucose (G), creatinine (C), and urea (U)as measured in our Raman instrument (FIG. 13). Spectra from 280-1750cm⁻¹ occupying 1051 CCD pixels were binned every 2 adjacent pixels toproduce Raman spectra of 525 data points each, reducing the size of thedata set for more rapid computation. Random concentrations uniformlydistributed between 0 and 10 were used to generate 60 mixture samplespectra, with zero-mean Gaussian white noise generated by MATLABsuperimposed on the spectra. The signal-to-noise ratio (SNR), definedhere as the ratio of the major Raman peak magnitude to the mean noisemagnitude, was ˜9. Half of the noise-added spectra formed thecalibration set, and the other half the prospective set. Differentcalibration methods were applied to the calibration set to generate theb vectors by minimizing the respective PRESS through leave-one-out crossvalidation. The b vectors were then used to calculate the SEP among theprospective set. Repeating this entire procedure, average SEP values andb vectors were obtained for different methods. In all calibrations, nomore than 5 factors were needed to obtain optimal prediction in PLS. Thepure analyte spectrum of glucose was used as the spectral constraint forCR. Additionally, because all sample-generating constituent analyteswere known, OLS was used to establish the best achievable prediction fora given SNR.

In a preferred embodiment, Raman spectra were acquired from 44water-dissolved mixture samples composed of glucose, creatinine, andurea, each with randomized concentration profiles from 0 to 50 mM, withrespective mean ˜25 mM. 22 samples were used for the calibration set andthe other 22 for the prospective set. Each sample was mixed from stocksolutions within 3 minutes of its spectrum being taken. All samples weremeasured in a 1-cm pathlength quartz cuvette using a Raman instrument.Each spectrum was acquired in 2 s with laser power equivalent to ˜30mW/mm² and a 1 mm² spot size at the sample. 30 spectra of eachwater-dissolved analyte and of water were acquired and averaged forbetter SNR. Pure analyte spectra were obtained by subtracting the waterand quartz spectra from the water-dissolved analyte spectra. Arepresentative sample spectrum (Sc) is displayed in FIG. 15 b vectorsobtained using different calibration methods were applied to theprospective set to calculate SEP. In all calibrations, no more than 5factors were needed to obtain optimal predictions in both PLS and HLA.The pure analyte spectrum of glucose was used as the spectral constraintfor both CR and HLA. Because of measurement errors in the pure analyteconcentrations (estimated at 1%), as well as to fully exploit HLA, weallowed the amplitude of the pure analyte spectra to vary within 10%.

In another preferred embodiment, the same protocol was followed, butwith turbid samples. Raman spectra were acquired from 54 water-dissolvedmixture samples composed of glucose, creatinine, India ink, andintralipid with a randomized concentration profile. Analyteconcentrations were varied between 5 and 30 mM with mean ˜16 mM. Theconcentration of India ink was varied such that the sample absorptioncoefficients ranged from 0.1 to 0.2 cm⁻¹ with mean ˜0.15 cm⁻¹. Theconcentration of intralipid was varied such that the sample scatteringcoefficients ranged from 35 to 75 cm⁻¹ with mean ˜55 cm⁻¹. The range ofoptical property changes agree well with reported values measured fromhuman skin. 27 samples were used for the calibration set and the other27 for the prospective set. A representative sample spectrum (S_(T)) isdisplayed in FIG. 15. In all calibrations, no more than 6 factors wereneeded to obtain optimal prediction in both PLS and HLA. It should bementioned that using prediction error (SEP) to compare results fromdifferent methods rather than cross validated error can effectivelyavoid false interpretation based on chance correlations and overfitting.

Two numerical methods of analysis were performed on spectra generatedfrom measured constituent analyte spectra, with glucose as the analyteof interest. The first analysis demonstrates that CR significantlyoutperforms PLS when all analyte concentrations vary in a randomfashion. The results, summarized in FIG. 16 (solid bars), show that withthe aid of prior information, CR generates lower SEP values than PLS.The reason for this is visualized in FIG. 17A, in which the deviation ofb_(PLS) and b_(CR) from the ideal b_(OLS) is plotted. Note that b_(CR)better converges to b_(OLS), therefore improving prediction over PLS.

The second simulation demonstrates that CR is less susceptible tospurious correlations among covarying analytes. The calibration data sethas been modified such that the concentration of the analyte of interestcorrelates to another analyte with R²˜0.5. The prospective set remaineduncorrelated. The results are displayed in FIG. 16, with the hatchedbars depicting the increased prediction error when the correlationsexist. It is observed that CR is much less affected by analytecorrelations than PLS.

SEP values for glucose obtained from PLS, BLA, and CR in the firstmeasurement with clear samples are summarized in FIG. 17B with solidbars. OLS values are not listed because the three constituent model doesnot account for all measurable variations, e.g. low amounts offluorescence from the quartz cuvette; therefore, OLS no longer providesthe best achievable performance. Among the implicit calibrationtechniques, substantial improvement over PLS is observed using thehybrid methods. CR and HLA generate similar SEP values, suggesting thatthese two methods have comparable performance under highly controlledexperimental conditions with clear samples.

SEP values obtained from PLS, HLA, and CR in the second experiment withturbid samples are summarized in FIG. 17B with hatched bars. Substantialimprovement over both PLS and HLA is observed using CR. The performanceof HLA is significantly impaired as a result of the turbidity inducedintensity variations of the analyte of interest. In HLA, the analyte ofinterest is assumed to be present in the data according to the referenceconcentrations. This assumption leads to the first and most importantstep: the removal of the spectral contribution of the analyte ofinterest from the data by subtracting the known spectrum of the analyteaccording to its concentration in each sample. As a result, theperformance in HLA depends on the “accuracy” of the constraint, as wellas the legitimacy of the assumption. In CR, however, the constraint onlyguides the inversion, allowing the minimization algorithm to arrive atthe optimal solution, thereby reducing its dependency on the accuracy ofthe constraint. Further, unlike HLA, which models the residual dataafter removing the analyte contribution, CR retains data fidelity and isunlikely to produce false built-in analyte spectral features in the bvector.

The results demonstrate that there is a tradeoff between maximizingprior information utilization and robustness concerning the accuracy ofsuch information. Multivariate calibration methods range from explicitmethods with maximum use of prior information (e.g. OLS, least robust),hybrid methods with a hard constraint (e.g. HLA), hybrid methods with asoft constraint (e.g. CR), and implicit methods with no priorinformation (e.g. PLS, most robust). CR achieves a preferred balancebetween these ideals for practical situations.

Constrained regularization is a hybrid method for multivariatecalibration. Strictly speaking, it should be categorized as an implicitcalibration method with one additional piece of information, thespectrum of the analyte of interest. In the broader context,regularization methods may perform somewhat better than either PLS orPCR for certain data structures. A heuristic explanation is thatregularization provides a continuous “knob”, and therefore can be usedto find a better balance between model complexity and noise rejection.These results show that in addition to this plausible intrinsicadvantage, an improvement can be obtained by incorporating a solutionconstraint.

CR significantly outperforms methods without prior information such asPLS and is less susceptible to spurious correlations with co-varyinganalytes. Compared to HLA, CR has superior robustness with less accuratespectral constraints. This robustness is crucial for hybrid methodsbecause it is difficult, if not impossible, to quantify high-fidelitypure analyte spectra in complex systems such as biological tissues.Further, CR naturally extends to situations in which pure spectra ofmore than one constituent are also known. In that case a better choiceof constraint (b_(o)) might be the net analyte signal calculated fromall the known pure spectra. CR is thus able to include more priorinformation without sacrificing the principal advantage of implicitcalibration: that only the reference concentrations of the analytes ofinterest are required in addition to the calibration spectra.

FIG. 18A shows a combination of constrained regularization (CR) and(IRS) corrected Raman spectra. The regression vector DCR is used togenerate spectra along with IRS spectra to provide improved calibratedmeasurements. FIG. 18B illustrates a method of employing constrainedregularization to calibrate a spectrum.

While the present invention has been described herein in conjunctionwith a preferred embodiment, a person with ordinary skill in the art,after reading the foregoing specification, can effect changes,substitutions of equivalents and other types of alterations to thesystems and methods that are set forth herein. Each embodiment describedabove can also have included or incorporated therewith such variationsas disclosed in regard to any or all of the other embodiments. Thus, itis intended that protection granted by Letters Patent herein be limitedin breadth only by the appended claims and any equivalents thereof.

What is claimed:
 1. A system for measurement of a blood analytecomprising: a first light source that emits Raman excitation light; asecond light source; a probe having a fiber optic light delivery andcollection system for coupling the first light source and the secondlight source onto a region of material having an endogenous bloodanalyte, the system collecting Raman light and reflected light returningfrom the region of material; a detector optically coupled to a proximalend of the probe that detects the collected Raman light and reflectedlight and generates Raman data and reflected light data; and aprocessing system connected to the detector such that the processingsystem receives the Raman data and the reflected light data, theprocessing system processing the Raman data, reflected light data and areference Raman spectrum of one or more blood analytes to providespectral data indicating a concentration of the endogenous blood analytein the region of material.
 2. The system of claim 1 wherein the detectordetects a diffuse reflectance spectrum and the processing systemdetermines a sampling volume within a subject.
 3. The system of claim 1wherein the first light source comprises a laser that emits at awavelength in a range of 750 nm to 1200 nm.
 4. The system of claim 1wherein the probe is coupled to a spectrograph.
 5. The system of claim 1wherein the processing system determines a ratio of Raman data anddiffuse reflectance data to provide corrected spectral data.
 6. Thesystem of claim 1 wherein the second light source emits broadband lightthat is coupled to the optical delivery system.
 7. The system of claim 1wherein the first light source comprises an infrared laser.
 8. Thesystem of claim 1 wherein the first light source emits in a range of 750nm to 1200 nm.
 9. The system of claim 1 wherein the fiber optic probe iscoupled to a fiber optic coupler.
 10. The system of claim 9 whereinsystem comprises a first fiber optic cable optically coupled to thefirst light source and a second fiber optic cable optically coupled tothe second light source, a combiner optically coupling the first fiberoptic cable and the second fiber optic cable to the probe fiber opticcable.
 11. The system of claim 1 wherein the detector measures areflectance spectrum including light reflected by the material at aRaman excitation wavelength that is emitted by the first light source.12. The system of claim 11 wherein the Raman excitation wavelength is ina range of 750 nm to 950 nm.
 13. The system of claim 11 wherein thedetector measures a Raman spectrum in response to the Raman excitationwavelength.
 14. The system of claim 11 further comprising a processorthat processes collected Raman data and collected reflected light datagenerated by the detector.
 15. The system of claim 14 further comprisingthe processor is adapted to condition the Raman data with the reflectedlight data.
 16. The system of claim 15 wherein the processor determinesan area of a reflected light spectrum about a Raman excitationwavelength.
 17. The system of claim 16 wherein the processor conditionsthe Raman data with said area.
 18. The system of claim 17 wherein theprocessor divides a Raman spectrum with said area.
 19. The system ofclaim 14 wherein the processor processes the Raman data to correct formeasurement volume variations in a plurality of Raman data measurements.20. The system of claim 1 further comprising a processor connected todetector that applies a transformation to a detected spectrum todetermine a blood analyte concentration.
 21. The system of claim 20further comprising the processor is adapted to define the transformationas a function including a spectral constraint.
 22. The system of claim21 wherein the spectral constraint comprises a spectrum of the analyte.23. The system of claim 22 wherein the analyte spectrum comprises aspectrum of glucose.
 24. The system of claim 21 wherein the spectralconstraint comprises a plurality of spectra of different analytes. 25.The system of claim 21 wherein the processor determines a minimizedfunction to define the transformation.
 26. The system of claim 20,wherein the processor processes the detected Raman spectrum and adetected reflectance spectrum in combination with the transformation toprovide a corrected spectrum.
 27. The system of claim 26 wherein thetransformation comprises a spectral constraint.
 28. The system of claim1 further comprising a calibration phantom.
 29. The system of claim 1further comprising the probe has a light delivery fiber and a pluralityof light collection optical fibers.
 30. The system of claim 29 whereinthe probe collects light returning from a region of skin of a subject.31. The system of claim 30 further comprising a reflector that couplesthe returning light into a plurality of collection fibers.
 32. Thesystem of claim 31 wherein the probe couples the returning light to aspectrograph, the spectrograph coupling dispersed light to the detector.33. The system of claim 29 wherein the probe has a distal filter. 34.The system of claim 1 further comprising a second detector that monitorslight source output.
 35. The system of claim 1 further comprising acontroller that actuates the first light source.
 36. The system of claim35 wherein the controller actuates a shutter device.
 37. The system ofclaim 1 wherein the includes a light path with a reflector.
 38. Thesystem of claim 1 wherein the second light source comprises a whitelight source that is coupled to the material for a reflectancemeasurement.
 39. The system of claim 1 further comprising a containerthat contains a sample of material removed from a body to be measured bythe system.
 40. A method of measuring a blood analyte comprising:illuminating a material with light from a first light source and asecond light source; detecting a Raman spectrum and a reflectancespectrum from the material in response to the illuminating light; andprocessing the detected Raman spectrum and the reflectance spectrum toprovide a processed spectrum to measure an endogenous blood analytewithin the material, the processing step including calculating aregularized spectrum using a reference Raman spectrum of one or moreblood analytes.
 41. The method of claim 40 further comprisingdetermining a concentration of an analyte with the processed spectrum.42. The method of claim 40 further comprising illuminating a sample witha laser excitation wavelength and detecting the reflected light spectrumand the Raman spectrum in response.
 43. The method of claim 40 furthercomprising illuminating a sample with a first narrowband light source tomeasure the Raman spectrum and a broadband light source to measure thereflectance spectrum.
 44. The method of claim 40 further comprisingstoring a plurality of reference spectra in a memory, each referencespectrum corresponding to a separate blood analyte.
 45. The method ofclaim 44 wherein the analytes comprise glucose, creatinine and urea. 46.The method of claim 40 further comprising delivering and a collectinglight using a fiber optic probe.
 47. The method of claim 46 furthercomprising collecting Raman light with a first plurality of collectionfibers and collecting reflected light with a second plurality ofcollection fibers.
 48. A system for measurement of a blood analytecomprising: a first light source that emits a Raman excitation light; asecond light source; an optical delivery system for coupling the firstlight source and the second light source through skin of a subject ontoa region of material having an endogenous blood analyte; an opticalcollection system that collects Raman and reflected light returning fromthe region in response to the Raman excitation light and light incidenton the region from the second light source; a detector that detects thecollected Raman and reflected light to generate Raman data and reflectedlight data; and a processing system connected to the detector such thatthe processing system processes the Raman data, the reflected light dataand a reference Raman spectrum of one or more blood analytes to measurethe endogenous blood analyte.
 49. The system of claim 48 wherein theprocessing system processes the Raman data and reflected light data toprovide spectral data indicating a concentration of analyte in theregion of material.
 50. The system of claim 49 wherein the processingsystem determines a ratio of Raman data and diffuse reflectance data toprovide corrected spectral data.
 51. The system of claim 48 wherein thedetector detects a diffuse reflectance spectrum and the processingsystem determines a sampling volume within a subject.
 52. The system ofclaim 48 wherein the first light source comprises a laser that emits ata wavelength in a range of 750 nm to 1200 nm.
 53. The system of claim 48wherein the optical collection system comprises a fiber optic couplerand a spectrograph.
 54. The system of claim 48 wherein the first lightsource emits a single narrowband light signal to induce a Raman lightemission and a reflected light emission that are detected by thedetector.
 55. The system of claim 54 wherein the detector detects thereflected light emission over a range of wavelengths of the narrowbandlight signal.
 56. The system of claim 48 wherein the first light sourcecomprises a laser.
 57. The system of claim 48 wherein the first lightsource emits in a range of 750 nm to 1200 nm.
 58. The system of claim 48wherein the optical delivery system comprises a fiber optic deliverysystem.
 59. The system of claim 48 wherein the detector measures areflectance spectrum including light reflected by the material at aRaman excitation wavelength that is emitted by the first light source.60. The system of claim 59 wherein the Raman excitation wavelength is ina range of 750 nm to 950 nm.
 61. The system of claim 48 wherein theprocessing system processes collected Raman data and collected reflectedlight data generated by the detector to determine a glucoseconcentration.
 62. The system of claim 61 wherein the processing systemdetermines an area of a reflected light spectrum about a Ramanexcitation wavelength.
 63. The system of claim 61 wherein the processingsystem processes the Raman data to correct for measurement volumevariations in a plurality of Raman data measurements.
 64. The system ofclaim 48 wherein the processing system processes the Raman data with thearea of a reflected light spectrum.
 65. The system of claim 64 whereinthe processing system divides a Raman spectrum with said area.
 66. Thesystem of claim 48 wherein the processing system applies atransformation to a detected spectrum to determine an analyteconcentration.
 67. The system of claim 66 wherein the processing systemapplies the transformation as a function including a spectralconstraint.
 68. The system of claim 67 wherein the spectral constraintcomprises a spectrum of the analyte.
 69. The system of claim 68 whereinthe analyte spectrum comprises a spectrum of glucose.
 70. The system ofclaim 48 wherein the collected reflected light has a spectrum in a rangeof 750 nm to 950 nm.